Editorial: Physiological, anatomical and sport performance adaptations to concentrated training periods in athletes
Notice bibliographique
Résumé
Understanding the dose-response to exercise has become the focal point of a sport scientists' responsibility. The complex physiology of an athlete requires practitioners to develop a comprehensive "fingerprint", unique to the athletes' personal response to exercise, training methodologies, and periodization. The dose-response to exercise as described by (Banister et al., 1992) typically involves an initial, brief period of fatigue, that can develop into long-term improvement in biological function if appropriate rest and recovery is provided. This model is largely based on Selye's explanation of the fundamental reaction to experiencing whole body stress over a continuous period, whereby positive adaptations or exhaustion can occur depending on the sustained stress experienced (Selye, 1950). The "dose" of exercise can be quantified into a training load metric using either wearable technology (Perrotta et al., 2018) or a subjective rating of perceived exertion (Perrotta et al., 2017;Perrotta & Warburton, 2018), that can be compared to the resulting physiological "response" (Perrotta et al., 2019;Perrotta, 2020). When examined together, practitioners can make informed decisions when prescribing impending training sessions depending on the "response" to the "dose" of exercise. For example, wearable devices that included GPS technology can accurately measure speed and distance of a training session (i.e. the "dose"). Combined with information from a simple heart rate monitor (i.e. the "response"), coaches and practitioners can periodically evaluate the heart rate vs. speed relationship in the field as an indirect marker of exercise economy/efficiency and may indirectly serve to indicate states of transient or long-lasting fatigue (Fletcher & Tomkins-Lane, 2021;Smith et al., 2002). Analyzing the exercise dose-response relationship can be valuable to coaches, athletes and practitioners. In a 2012 survey of coaches and sport science support staff, Taylor et al., (2012) showed that 91 per cent of respondents used some form of a training monitoring system. In this survey, 70 per cent of these respondents indicated the focus was on 'load quantification' and the monitoring of fatigue or recovery to "prevent overtraining, reduce injuries, monitor the effectiveness of the training programs and ensure maintenance of performance" (Taylor et al., 2012). This Research Topic of Frontiers in Physiology and Sports and Active Living, contains four manuscripts where the primary purpose focused on analyzing the exercise dose-response relationship to improve athletic development. Three original articles (Perrotta et combination of the two offered greater strength and power gains. They demonstrate that all three strength intervention strategies equally improved upper and lower body strength and power, with only significant group differences found between peak power output at 30% of 1 repetition max. This may suggest that unilateral or a combination unilateral and bilateral strength training regime is superior to bilateral training alone, at least for this specific strength and power measure. This special topic edition provides coaches and practitioners novel insight into the dose-repose to various forms of exercise, and the interplay between physiological function and human performance. Although adaptations from exercise are often evaluated using a pre and post mesocycle approach, this form of assessment is retrospective in nature, and limits the understanding of athlete's response over duration of the training period. Unremitting developments in wearable technology allow integrative support staff to monitoring athletes physiological response during and post exercise (i.e. dose-response) in real-time. This immediate feedback allows evidence-based decisions towards adapting present training sessions to mitigate fatigue, or include additional exercise, to ensure athletic development over the course of each mesocycle. Sports scientists working within a team environment are encouraged to published comprehensive data sets that display the utility of wearable technology for examining the dose-response from daily training in elite athletes. Taking together, this information can develop consensus statements towards establishing best practice guidelines for integrative support staff working within professional, national and provincial sporting organizations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,003 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,016 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».